In recent years, two different approaches for learning register automata have been developed:
as part of the LearnLib tool algorithms have been implemented that are based on the Nerode congruence
for register automata, whereas the Tomte tool implements algorithms that
use counterexample-guided abstraction refinement to automatically construct appropriate mappers.
In this paper, we compare the LearnLib and Tomte approaches
on a newly defined set of benchmarks and highlight their differences and respective strengths.